Christine Doig on Data Science as a Team Discipline

Srini Penchikala | Info Q | August 26, 2016

Data science is about the design and development of solutions to extract insights from data (structured and unstructured) using machine learning and predictive analytics techniques and tools. Data Science as a discipline and Data Scientist as a role have been getting lots of attention in the recent years to solve real world problems with solutions ranging from fraud detection to recommendation engines.

Christine DoigChristine Doig, Senior Data Scientist at Continuum Analytics, spoke at this year’s OSCON Conference about data science as a team discipline and how to navigate the data science Python ecosystem. She talked about how to transition from data to models to applications. Christine also discussed the different roles and skillsets needed for the data science discipline: Statistician, Computational Scientist, and Developer.

She elaborated on the deliverables of these different roles.

  • Statistician: Insights, predictions, visualizations
  • Computational Scientist: Algorithms, libraries, performance
  • Developer: Software, applications, containers

Data science teams face challenges in different areas like collaboration, big data, deployment and sharing insights.

  • Collaboration: Get diverse data teams (languages, tools, data models, deliverables) to collaborate effectively
  • Big Data: Move Data Scientists (Stats / Analyst) to use Big Data infrastructure
  • Deployment: Deploy predictive models into production applications
  • Sharing insights: Share insights with decision makers

She also spoke about Continuum Analytics' contributions to the Python ecosystem with frameworks like Bokeh, Datashader, Dask and Blaze...